ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning

TL;DR

ViTaS introduces a novel visuo-tactile fusion framework using contrastive learning and CVAE to improve robotic manipulation, especially under occlusion, by effectively leveraging the complementary nature of visual and tactile data.

Contribution

The paper proposes Soft Fusion Contrastive Learning and a CVAE module to enhance visuo-tactile feature alignment and complementarity for improved robotic manipulation.

Findings

Outperforms existing baselines in simulated environments

Achieves significant improvements in real-world tests

Effectively handles occlusion scenarios

Abstract

Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly focus on the alignment of visual and tactile features and the integration mechanism tends to be direct concatenation. Consequently, they struggle to effectively cope with occluded scenarios due to neglecting the inherent complementary nature of both modalities and the alignment may not be exploited enough, limiting the potential of their real-world deployment. In this paper, we present ViTaS, a simple yet effective framework that incorporates both visual and tactile information to guide the behavior of an agent. We introduce Soft Fusion Contrastive Learning, an advanced version of conventional contrastive learning method and a CVAE module to utilize the alignment and complementarity within visuo-tactile representations. We demonstrate the effectiveness of our method in 12 simulated and 3 real-world environments, and our experiments show that ViTaS significantly outperforms existing baselines. Project page: https://skyrainwind.github.io/ViTaS/index.html.